Tyler Beason

Tyler Beason

Finance PhD Candidate

Arizona State University

About Me

My research interests include studying financial markets in the large and in the small.

  • Asset Pricing

  • Tail Risk

  • Macrofinance

  • Algorithmic Trading

  • Computational Finance

I maintain a public Call For Papers calendar that might interest you.

My wife and I enjoy spending our free time entertaining our son and getting outside when we can.


Working Papers

Consumption, Dividends, Labor Income, and Risk Premia
Job Market Paper Draft coming soon.

I provide a modification to the cash flow process which makes any discrete time representative agent consumption-based asset pricing model simultaneously consistent with a number of stylized facts about aggregate labor income growth, labor's share of consumption, and growth rates in stock market dividends. The modification is parsimonious in that it adds only 4 parameters which are directly tied to observable features of the labor share. When applied to existing leading asset pricing models, the modified models continue to match the commonly-targeted macroeconomic moments, but also reproduce the term structures of growth rate volatility, and of risk premia, and provide a partial explanation of recent evidence regarding asymmetric dependence between consumption and dividend growth rates.

On Sources of Risk Premia in Representative Agent Models
with David Schreindorfer. SSRN R&R at the Journal of Political Economy

We use options and return data to decompose unconditional risk premia into different parts of the return state space. In the data, the entire equity premium is attributable to monthly returns below -11.3%, but returns in the extreme left tail matter very little. In contrast, leading asset pricing models based on habits, long-run risks, and rare disasters attribute the premium almost exclusively to returns above -11.3%, or to the extreme left tail. We find that model extensions with a larger quantity of tail risk cannot account for the data, while models with a higher price of tail risk can.

The Anatomy of Trading Algorithms
with Sunil Wahal. SSRN

We study the anatomy of four widely used standardized institutional trading algorithms representing $675 billion in demand from 961 institutions between 2012 and 2016. The central tradeoff in these algorithms is between the desire to trade and transaction costs. Large parent orders generate hundreds of child orders which strategically employ the price, time, and display priority rules embodied in market structure to navigate this tradeoff. The distribution of child orders is non-random, generating strategic runs which oscillate between providing and taking liquidity. Price impact occurs both at the time an order is submitted to the book (regardless of whether it is filled), and at the time of execution. Passive child orders have much lower likelihood of execution but still incur substantial price impact. Conversely, marketable orders, even though immediately executable, do not necessarily guarantee execution and generate even larger price impact.

Heterogeneity and Household Portfolio Choice
Draft coming soon.

I study the distributional properties of household risky shares, the fraction of their financial portfolio allocated to risky assets. Many proposed solutions to bring household life-cycle portfolio choice models in line with the average risky share, such as participation costs or differences in labor income risk profiles, fall far short of generating sufficient cross-sectional heterogeneity in portfolio allocations at nearly every point in the life-cycle.

Terminal Wealth in the Presence of Portfolio Contributions
Draft coming soon.

I provide a clean, tractable way to evaluate the moments and risk exposures of future financial wealth. The analysis operates under minimal assumptions about the financial portfolio returns and does not require the use of Monte Carlo simulations or assumptions about investor risk preferences. Specific applications include saving for retirement or college expenses and participation in ESOPs.

Pre-PhD Work

Simulation of a Financial Market: The Possibility of Catastrophic Disequilibrium
with Amit Sinha, Kelly Roos, & Philip Horvath. Chaos, Solitons, & Fractals, 2019, 125, 13-16. Publisher Link

We use kinetic Monte Carlo simulations to produce solutions of an agent-based, rate equation model of an informationally efficient, closed financial market. The simulations produce a crash in the market that is forewarned through the observation of a market instability from which the market temporarily recovers. The market remained in a quasi-stable state for a relatively large amount of time between the warning and the crash, raising the prospect that some mitigating action can be taken in time to avert the impending crash. This result has strong ramifications for the future of predicting calamitous market events, especially if some observable aspect of financial markets can be positively identified and associated with simulation parameters.


I am a proponent of open source software and transparent academic research. I am an active member of the Julia community.

In addition to contributing to existing Julia packages, I maintain a number of packages I found useful in my own work. I hope that other finance researchers and practitioners can find value in them as well.

This website is also open source.